Risk Factors Associated With Accelerated Rubella IgG Antibody Loss in Previously Vaccinated, Treatment‐Naive Patients With Juvenile Systemic Lupus Erythematosus: A Prospective Study

2019 ◽  
Vol 71 (6) ◽  
pp. 1022-1023 ◽  
Author(s):  
Despoina N. Maritsi ◽  
Irene Eleftheriou ◽  
George Vartzelis ◽  
Nikos Spyridis ◽  
Maria N. Tsolia
2009 ◽  
Vol 36 (11) ◽  
pp. 2454-2461 ◽  
Author(s):  
ROBERT J. GOLDBERG ◽  
MURRAY B. UROWITZ ◽  
DOMINIQUE IBAÑEZ ◽  
MANDANA NIKPOUR ◽  
DAFNA D. GLADMAN

Objective.To ascertain coronary artery disease (CAD) outcomes and predictive factors in a prospective study of patients with systemic lupus erythematosus (SLE) and matched healthy controls.Methods.SLE patients and non-SLE age-matched controls without a history of CAD were recruited into a prospective study between 1997 and 1999. CAD events were assessed at clinic visit for SLE patients and through telephone interview and chart review for controls. All events were verified with patient medical records.Results.Followup information was available on 237 controls and 241 SLE patients. The mean followup time was 7.2 years. Univariate analyses identified age and postmenopausal status as predictors of CAD in both the groups. Sedentary lifestyle, hypertension, the presence of metabolic syndrome, and the number of Framingham risk factors were predictive in the control group only. The 10-year risk of CAD score was predictive in both groups but was not as marked in the SLE group as in the controls. None of the lipid subfractions were predictive for CAD in the SLE group, whereas in the controls, a high triglyceride level ≥ 2.8 was predictive. Time-to-event multivariate analysis for CAD in all subjects revealed SLE itself, older age, and triglycerides ≥ 2.8 to be highly predictive for CAD.Conclusion.In a prospective study of patients with SLE and matched controls followed over a median of 8 years, patients with SLE developed significantly more CAD events than controls. Accounting for demographic variability, CAD risk factors, and lipid factors, SLE is an independent risk factor for the development of CAD.


Author(s):  
Asma Al-Kindi ◽  
Batool Hassan ◽  
Aliaa Al-Moqbali ◽  
Aliya Alansari

Angiology ◽  
2005 ◽  
Vol 56 (5) ◽  
pp. 601-609 ◽  
Author(s):  
Tomohiro Akimoto ◽  
Shigeto Kobayashi ◽  
Naoto Tamura ◽  
Toshiya Ohsawa ◽  
Terunaga Kawano ◽  
...  

1993 ◽  
Vol 48 (6) ◽  
pp. 388-390 ◽  
Author(s):  
A TINCANI ◽  
D FADEN ◽  
M TARANTINI ◽  
A LOJACONO ◽  
P TANZI ◽  
...  

2009 ◽  
Vol 36 (11) ◽  
pp. 2476-2480 ◽  
Author(s):  
MICHELLE PETRI ◽  
SUKHMINDER SINGH ◽  
HANNA TESFASYONE ◽  
ASHIMA MALIK

Objective.We determined the prevalence of and risk factors for British Isles Lupus Activity Group (BILAG) flare in patients with systemic lupus erythematosus (SLE).Methods.We followed 299 patients for 1 year with the BILAG scores calculated using British Lupus Integrated Prospective System software and confirmed with manual calculation.Results.“A” flares occurred at a rate of 0.254/year, “B” flares 1.637/year, and A or B flares 1.765/year. The most common A flares were renal and mucocutaneous. The most common B flares were hematologic, renal, mucocutaneous, and musculoskeletal. Risk factors for a later A or B flare in the hematological system included: low C3 (p < 0.0001), low C4 (p = 0.0004), and positive anti-double-stranded (ds)DNA (p = 0.003); in the mucocutaneous system: low C3 (p = 0.02) and low C4 (p = 0.0004); and in the renal system: low C3 (p = 0.02) and low C4 (p = 0.02). In a stepwise regression model, only ethnicity (p = 0.02) and low C4 (p = 0.0002) remained as independent predictors of later A or 2B flares.Conclusion.The organ system distribution of A and B flares is very different, with A flares more common in renal and mucocutaneous, and B flares more common in hematologic and renal systems. A or 2B flares are significantly more common in African Americans and in patients with abnormal serologies (low C3, low C4, or high anti-dsDNA). If flare is an outcome in an SLE clinical trial, these factors must be balanced by taking them into account at baseline in terms of randomization, or by statistical adjustment in final analyses.


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